GainTKW: A Measurement System of Thousand Kernel Weight Based on the Android Platform

Wu, Wenhua; Zhou, Lei; Chen, Jian; Qiu, Zhengjun; He, Yong

doi:10.3390/agronomy8090178

Cited by 44 publications

(43 citation statements)

References 31 publications

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“…Thousand kernel weight (TKW) is one of the main contributors of grain yield [55] and less reduction in TKW enables plants to maintain ideal yield under drought conditions [56]. Liu et al [57] reported positive association of TKW with grain yield under irrigated and drought conditions indicating that plant genotypes having higher TKW under irrigated conditions often have a chance to maintain higher TKW under drought conditions.…”

Section: 4in Silico Resultsmentioning

confidence: 99%

GWAS Analysis of Wheat Pre-breeding Germplasm for Terminal Drought Stress Using Next Generation Sequencing Technology

Shokat¹,

Sehgal²,

F³

et al. 2020

Preprint

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Bread wheat (Triticum aestivum L.) is one of the most important cereal crops for food security. Of all the stresses that curtail wheat productivity, drought has the most detrimental effects. Especially terminal drought stress i.e. at the time of flowering imposes a big challenge to sustain grain production. In the current study, 339 pre-breeding lines derived from three-way crosses of exotics x elite lines were evaluated in the irrigated and drought stress environments at Obregon, Mexico for the year 2016 and 2018. Drought significantly reduced yield (Y), spike length (SL), number of grains per spikes (NGS) and thousand kernel weight (TKW) by 46.4, 19.2, 23.5 and 25.9%, respectively in comparison to irrigated conditions. Kernel abortion (KA), highly correlated with Y, increased significantly (11.6%) under drought stress environment. Population structure analysis in this panel revealed three sub-populations and a genome wide linkage disequilibrium (LD) decay was at 2.5 cM. Single marker and haplotypes-based genome wide association study (GWAS) revealed significant associations on three chromosomes; 4A (HB10.7), 2D (HB6.10) and 3B (HB8.12) with Y, SL and TKW, respectively. Likewise, associations on chromosomes 6B (HB17.1) and 3A (HB7.11) were identified for NGS and on 3A (HB7.12) for KA. Five traits i.e. normalized difference vegetation index (NDVI), canopy temperature depression (CTD) days to heading (DTH), NGS, KA were associated at chromosome 3A both under irrigated and drought conditions however, different haplotypes were estimated. Twenty-six SNPs were part of 10 haplotype blocks associated with Y, SL, TKW, NGS and KA. In silico analysis of the associated SNPs/haplotypes showed hits with candidate genes known to confer abiotic stress resistance in model species and crops. Potential candidate genes include those coding for sulfite exporter TauE/SafE family in Arabidopsis thaliana, TBC domain containing protein in Oryza sativa subsp. Japonica and heat shock proteins in Aegilops tauschii subsp. tauschii were revealed. The SNPs linked to the promising genes identified in the study can be used for marker-assisted selection.

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Section: 4in Silico Resultsmentioning

confidence: 99%

GWAS Analysis of Wheat Pre-breeding Germplasm for Terminal Drought Stress Using Next Generation Sequencing Technology

Shokat¹,

Sehgal²,

F³

et al. 2020

Preprint

View full text Add to dashboard Cite

show abstract

“…Some researchers use either computer software programs or smartphone apps that count kernel numbers and estimate grain kernel traits in given digital images (Tanabata et al 2012;Whan et al 2014;Komyshev, Genaev, and Afonnikov 2017;Gao et al 2017;Wu et al 2018;Rasband 1997). Most of these software packages have strict requirements for image background and grain distribution.…”

Section: Introductionmentioning

confidence: 99%

GridFree: A Python Package of Image Analysis for Interactive Grain Counting and Measuring

Zhang

2020

Preprint

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Grain characteristics, including kernel length, kernel width, and thousand kernel weight, are critical component traits for grain yield. Manual measurements and counting are expensive, forming the bottleneck for dissecting the genetic architecture of these traits toward ultimate yield improvement. High-throughput phenotyping methods have been developed by analyzing images of kernels. However, segmenting kernels from the image background and noise artifacts or from other kernels positioned in close proximity remain challenges. In this study, we developed a software package, named GridFree, to overcome these challenges. GridFree uses an unsupervised machine learning approach, K-Means, to segment kernels from the background by using principal component analysis on both raw image channels and their color indices. GridFree incorporates users' experiences as a dynamic criterion to set thresholds for a divide-and-combine strategy that effectively segments adjacent kernels. When adjacent multiple kernels are incorrectly segmented as a single object, they form an outlier on the distribution plot of kernel area, length, and width. GridFree uses the dynamic threshold settings for splitting and merging. In addition to counting, GridFree measures kernel length, width, and area with the option of scaling with a reference object. Evaluations against existing software programs demonstrated that GridFree had the smallest error on counting seeds for multiple crops, including alfalfa, canola, lentil, wheat, chickpea, and soybean. GridFree was implemented in Python with a friendly graphical user interface to allow users easily visualize the outcomes and make decisions, which ultimately eliminates time-consuming and repetitive manual labor. GridFree is freely available at the GridFree website (https://zzlab.net/GridFree).

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“…The efficiency of plant phenotyping can be increased by technologies of digital image analysis [10][11][12]. These technologies were applied for both kernel size and shape morphometry [13][14][15][16] and analysis of the spike traits [17][18][19][20].The methods for digital image analysis of spike characteristics are also developed and allow for solving of different problems. Grillo et al [17] developed a method for the wheat variety identification using glumes size, shape, color, and texture characteristics obtained from image analysis.…”

mentioning

confidence: 99%

Morphometry of the Wheat Spike by Analyzing 2D Images

et al. 2019

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Spike shape and morphometric characteristics are among the key characteristics of cultivated cereals associated with their productivity. Identification of the genes controlling these traits requires morphometric data at harvesting and analysis of numerous plants, which could be automatically done using technologies of digital image analysis. A method for wheat spike morphometry utilizing 2D image analysis is proposed. Digital images are acquired in two variants: a spike on a table (one projection) or fixed with a clip (four projections). The method identifies spike and awns in the image and estimates their quantitative characteristics (area in image, length, width, circularity, etc.). Section model, quadrilaterals, and radial model are proposed for describing spike shape. Parameters of these models are used to predict spike shape type (spelt, normal, or compact) by machine learning. The mean error in spike density prediction for the images in one projection is 4.61 (~18%) versus 3.33 (~13%) for the parameters obtained using four projections.Agronomy 2019, 9, 390 2 of 22 the more so as the modern experiments involve tens of thousands of plants [5,9]. Correspondingly, automation of this laborious and time-consuming process is relevant for the science and breeding. The efficiency of plant phenotyping can be increased by technologies of digital image analysis [10][11][12]. These technologies were applied for both kernel size and shape morphometry [13][14][15][16] and analysis of the spike traits [17][18][19][20].The methods for digital image analysis of spike characteristics are also developed and allow for solving of different problems. Grillo et al. [17] developed a method for the wheat variety identification using glumes size, shape, color, and texture characteristics obtained from image analysis. Makanza et al. [18] designed the software allowing for determination of ear length and width as well as estimation of maize grain size and weight. Pound et al. [19] used deep learning to count wheat spikes and assess the number of spikelets per spike using images of wheat plants taken in glasshouse conditions. However, Pound et al. in their work did not estimate the morphological characteristics of spikes, such as their length, width, and type. As for the deep learning algorithms, their use, in turn, requires a large number of annotated spike images (several tens of thousands) to train the neural network parameters. Hughes et al. [20] determined wheat spike and grain morphometric parameters from X-ray micro computed tomography data. This method is highly accurate when determining the fine characteristics of spike and grain shapes but requires a special device for recording tomographic images. Kun et al. [21] proposed morphometry of wheat spikes via image processing. The authors utilized 2D images to assess various characteristics, such as spike length and awn number and length, and classified the spike shape type according to its length-to-width ratio. The spike parameters were used for their classification according t...

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GainTKW: A Measurement System of Thousand Kernel Weight Based on the Android Platform

Cited by 44 publications

References 31 publications

GWAS Analysis of Wheat Pre-breeding Germplasm for Terminal Drought Stress Using Next Generation Sequencing Technology

GWAS Analysis of Wheat Pre-breeding Germplasm for Terminal Drought Stress Using Next Generation Sequencing Technology

GridFree: A Python Package of Image Analysis for Interactive Grain Counting and Measuring

Morphometry of the Wheat Spike by Analyzing 2D Images

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